Abstract
This paper investigates the land value creation potential from flood mitigation investments in a theoretical and applied setting, using the urban area of Buenos Aires as a case study. It contributes to the literature on the wider economic benefits of government interventions and the dividends of resilience investments. Using a simple urban economics framework that represents land and housing markets, it finds that not all flood mitigation interventions display the same potential for land value creation: where land is more valuable (city centers for example), the benefits of resilience are higher. The paper also provides ranges for land value creation potential from the flood mitigation works in Buenos Aires under various model specifications. Although the estimates vary largely depending on model parameters and specifications, in many cases the land value creation would be sufficient to justify the investments. This result is robust even in the closed city configuration with conservative flood damage estimates, providing that the parameters remain reasonably close to the values obtained from the calibration. Finally, acknowledging that fully calibrating and running an urban simulation model is data greedy and time intensive – even a simple model as proposed here – this research also proposes reduced form expressions that can provide approximations for land value creation from flood mitigation investments and can be used in operational contexts.
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Data Availability
The parameter values used in the modeling are all disclosed and discussed in this study. The land use map, the land use exclusion map, the CABA housing regulations and their translations into FARs, the travel time and travel cost matrices for all transport modes are available from the corresponding author on reasonable request. The other analyzed and processed datasets of the current study are not publicly available, as they were shared by third parties for their sole use in this research, but may be made available from the corresponding author on reasonable request and providing third party agreement. This is the case for the flood maps and the databases on land values and population densities.
Notes
This redistribution of land values across the urban area carries potential consequences for various administrative levels if they rely on land value taxes for own source revenues. We acknowledge that this is an important consideration but choose in this paper to focus only on the net aggregate impact of flood mitigation works.
The appropriate functional form for the housing construction function has been widely debated in the literature with some scholars arguing that a CES with a substitution elasticity between capital and land inputs below one is a better fit because capital to land ratios increase at a slower rate than land prices (Larson and Yezer 2015). However, measuring land values and invested capital appropriately is uneasy and creates empirical challenges which are likely to bias estimates of the substitution elasticity downward. Works that have paid closer attention to this measurement problem tend to find much higher substitution elasticities and ones that are very close to 1, meaning that Cobb-Douglas functions, although not perfect, are an appropriate representation for housing production functions (Thorsnes 1997; Epple et al. 2010; Ahlfeldt and McMillen 2014; Combes et al. 2021).
Note that this is distinct from saying that household utilities are identical in cities with Absentee Landowners or Public Ownership of land: they aren’t. But they are reduced in the same proportion when compared to their ‘no-flood’ situations, which constitute distinct baselines in CCA and CCP models.
Some of the following paragraphs in section 3.1 describing the NEDUM-2D model appear in a similar form in Avner et al. (2017). For the purposes of making this paper self-standing, it was deemed important to include them here as well.
This mono-centric hypothesis is a clear simplification but that finds some support in the data for the specific case of Buenos Aires, the Buenos Aires region displays a strong mono-centric structure. For instance, in 2012 more than 40% of all jobs in the Buenos Aires region are localized in the Ciudad Autónoma de Buenos Aires (CABA) even though CABA only represents 5.3% of Region Metropolitana’s area.
For a version of NEDUM-2D that represents multiple job centers and household types differing by annual average income and work location see Pfeiffer et al. (2019).
We would expect 60 distinct flood maps for each flood depth, return period and in the absence or after flood protection works: 5x6x2 = 60. However, after flood protection works, there are no locations that get flooded every two years with a water depth of 160 cm, therefore we have 59 flood maps rather than 60.
This pattern has some very limited exceptions: out of the sum of 10,699 instances where a grid cell is impacted by a specific flood event (before resilience investments), only in 97 of these do we observe unusual behaviors where a bigger flood-depth is not contained within the area of a smaller flood-depth for any given return periods. We also find that out of the 961 grid cells that get flooded in part or in whole by at least one flood event (before flood mitigation), in only 31 of these does the flood data show bigger flood depth areas that are larger than smaller flood depths. These localized discrepancies can possibly be explained by some small errors in the hydrological modeling or the transcription of these results into shapefiles. We deal with these outliers by assuming that a concentric ring for a smaller flood-depth cannot have a negative value: sj, τ, d, w ≥ sj, τ, d + 1, w.
The expression of \({\rho}_{f_{j,w}}\) will result in a lower bound estimate of the costs of floods on structures because, in the absence of good information on the form of the function linking damages and flood return periods, we do not fully integrate the area below the damage curve function. Making the assumption that damages increase linearly with flood return periods would allow us to perform a complete integration of damages but would undoubtedly overestimate the costs of floods.
We choose ‘masonry’ damage as the appropriate estimate of damages to buildings. As the floods occur in the central part of the urban area of Buenos Aires, it is reasonable to assume that most structures are built from solid materials (such as reinforced concrete) and engineered. With lower quality housing (built from timber for example), floods increase the damages as a function of the initial building costs. This estimate is however conservative as we disregard the damages to the contents of dwellings.
A large sensitivity analysis, using techniques adopted for Decision Making under Uncertainty, is performed on most calibrated parameters of the model to explore the robustness of our results as well as which parameters drive our results. This analysis is documented in section 6. It is shown that parameter b, which intervenes in the construction cost function and the real interest rate i have strong impacts on our results and that land value creation ranges from approximately US$ 6 thousand to US$ 4.5 billion depending on their values (excluding 3 of 3000 runs that return negative values).
see the project appraisal document of the project: https://documents.banquemondiale.org/fr/publication/documents-reports/documentdetail/287961468328119648/argentina-flood-risk-management-support-project-for-the-autonomous-city-of-buenos-aires.
The numbers displayed for land value creation in sections 4.1 and 4.2 differ because section 4.1 reports land value creation potential for the lifetime of the structure investments whereas section 4.2 reports annual land value creation in order to be comparable with annual avoided damages. Total land value creation is equivalent to annual land value creation divided by the real interest rate, they both entertain a very direct relationship (see equation (3)).
Accessible at: https://www.worldpop.org/methods/populations and https://landscan.ornl.gov/.
Local land value increases, as opposed to aggregate net land value change, can occur in the very short term as soon as the flood mitigation works have been announced even, consistent with the assumption of land prices corresponding to their highest and best use and developers/landowners’ perfect foresight. We are however interested in aggregate land value creation after land and housing market mediation, which can only manifest itself over time.
We use a Latin Hypercube Sampling (LHS) function and make use of the option to minimize the sum of between-column squared correlations.
Table 4 also presents the values and the signification of the main parameters of the NEDUM-2D model applied to Buenos Aires in our baseline calibration. It also explains how these were obtained (through calibration processes, informed by data, borrowing from the literature or through assumptions), and how these parameter values relate to those found in the literature.
In most scenarios of the sensitivity analysis, this is not the case as most scenarios show land value creation potential to be higher than avoided damages (see online supplement).
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Acknowledgments
We would like to thank the World Bank Argentina Country Management Unit for their interest in this study and in particular Veronica Raffo, Maria Catalina Ramirez and Pablo Iribarren Santos for sharing with us the flood maps before and after resilience investments in Buenos Aires. We are grateful to Jun Rentschler and Marguerite Obolensky for their comments and inputs along the way. We are indebted to Maria Catalina Ramirez, Francis Fragano and Steven Rubinyi for their peer-review of and useful comments on a previous draft version. This study was financially supported by the Global Facility for Disaster Reduction and Recovery (GFDRR).
Funding
The authors would like to express gratitude for the financial support from the Global Facility for Disaster Reduction and Recovery (GFDRR), through its Multi-Donor Trust Fund and USAID.
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Avner, P., Viguié, V., Jafino, B.A. et al. Flood Protection and Land Value Creation – Not all Resilience Investments Are Created Equal. EconDisCliCha 6, 417–449 (2022). https://doi.org/10.1007/s41885-022-00117-7
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DOI: https://doi.org/10.1007/s41885-022-00117-7
Keywords
- Land value creation
- Resilience investments
- Floods
- Triple dividend
- Urban economics
- Land use – Transport integrated models